Application of Seismic Attribute Technique to estimate the 3D model of Hydraulic Flow Units: A case study of a gas field in Iran

Application of Seismic Attribute Technique to estimate the 3D model of Hydraulic Flow Units A case study of a gas field in Iran Egyptian Journal of Petroleum xxx (2017) xxx–xxx Contents lists availabl[.]

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Full Length Article

Application of Seismic Attribute Technique to estimate the 3D model of

Hydraulic Flow Units: A case study of a gas field in Iran

Mohamad Iravania, Mahdi Rastegarniab, Dariush Javanic, Ali Sanatid,⇑, Seyed Hasan Hajiabadid

a Department of Petroleum Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran

b

Department of Petrophysics, Pars Petro Zagros Engg & Services Company, Tehran, Iran

c

Department of Mining Engineering, Imam Khomeini International University, Ghazvin, Iran

d

Faculty of Petrochemical and Petroleum Engineering, Hakim Sabzevari University, Sabzevar, Iran

a r t i c l e i n f o

Article history:

Received 20 January 2017

Revised 6 February 2017

Accepted 14 February 2017

Available online xxxx

Keywords:

Multi-attribute analysis

Seismic attribute

Flow Zone Index

Acoustic impedance volume

Hydraulic Flow Units

a b s t r a c t One of the most important steps in evaluation and development of hydrocarbon reservoirs is the mapping

of their characteristics Nowadays, Seismic Attribute Technique is used to build parameters of hydrocar-bon reservoirs in inter-well spaces One of these parameters is the Flow Zone Index (FZI) that has a sig-nificant effect on different stages of evaluation, completion, primary and secondary production, reservoir modeling and reservoir management The aim of this study is to introduce an equation using seismic attribute and FZI log in wells and then generalize it to predict FZI throughout the reservoir For this pur-pose, acoustic impedance (AI) volume as an external attribute was created while internal attributes were computed from seismic data After that, The best set of attributes was determined using stepwise regres-sion after which seismic attributes were applied to multi-attribute analysis to predict FZI Then, the attri-bute map resulted from multi-attriattri-bute analysis was used to interpret the spatial distribution of the gas bearing carbonate layers Finally, the optimum number of Hydraulic Flow Units (HFU) was determined by analyzing the break point in the plot of cumulative frequency of FZI for wells and was generalized all over the reservoir by using the 3D HFU model The results demonstrated that multi-attribute analysis was a striking technique for HFU estimation in hydrocarbon reservoirs that reduces cost and increases rate of success in hydrocarbon exploration Distribution of producible hydrocarbon zones along with the seismic lines around the reservoir was characterized by studying this model which can help us in choosing the location of new wells and more economical drilling operations

Ó 2017 Egyptian Petroleum Research Institute Production and hosting by Elsevier B.V This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

1 Introduction

One of the most important steps in evaluation and development

of hydrocarbon reservoirs is the mapping of their characteristics

Numerous empirical relations between seismic attributes and well

log data have been introduced for estimation of physical properties

such as porosity, permeability, etc[1–7] Multi-attribute analysis is

an effective method to use well logs in conjunction with seismic

data for prediction of well log properties from the seismic

responses In this paper multi-attribute method is used to examine

the prediction of Flow Zone Index (FZI) logs from seismic

attri-butes Permeability and FZI has a significant effect on different

stages of evaluation, completion, primary and secondary

produc-tion, reservoir modeling and reservoir management Therefore, different methods have been used to evaluate FZI by petroleum engineers [8–13] Rezaee et al introduced a new approach to determine hydraulic flow unit (HFU) by the current zone indicator (CZI) and electrical flow unit (EFU) concepts[14]

Recently, Dezfoolian et al presented an intelligent model based

on probabilistic neural networks (PNN) to produce a quantitative formulation between seismic attributes and HFU in Kangan and Dalan carbonate reservoirs [15] Rastegarnia and Kadkhodaie-Ilkhchi used seismic attribute analysis to predict FZI using seismic and well log data They showed that it is an effective technique to predict FZI in an oil reservoir[16] Moreover, Yarmohammadi et al delineated high porosity and permeability zones by using the seis-mic derived flow zone indicator data at the Shah Deniz sandstone packages[17] Rastegarnia et al studied the application of seismic attribute analysis and neural Network to estimate the 3D FZI and electrofacies of a hydrocarbon reservoir They found that these

http://dx.doi.org/10.1016/j.ejpe.2017.02.003

1110-0621/Ó 2017 Egyptian Petroleum Research Institute Production and hosting by Elsevier B.V.

This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ).

Peer review under responsibility of Egyptian Petroleum Research Institute.

⇑ Corresponding author.

E-mail address: ali.sanati@yahoo.com (A Sanati).

Egyptian Journal of Petroleum xxx (2017) xxx–xxx

Contents lists available atScienceDirect

Egyptian Journal of Petroleum

j o u r n a l h o m e p a g e : w w w s c i e n c e d i r e c t c o m

Please cite this article in press as: M Iravani et al., Application of Seismic Attribute Technique to estimate the 3D model of Hydraulic Flow Units: A case

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methods are successful in modeling of FZI and electrofacies from

3D seismic data[18]

In this study, FZI was estimated by use of seismic attribute

anal-ysis in one of Iranian gas fields For this purpose, acoustic

impedance volume as an external attribute was created and the internal attributes were computed from seismic data The best set of attributes was determined using stepwise regression Seismic attributes were applied to multi-attribute analysis to predict FZI The attribute map resulted from multi-attribute analy-sis was then used to interpret spatial distribution of the gas bear-ing carbonate layer Petrophysical data of two wells, 3D seismic data cube, and structurally interpreted data from a gas field in the southwest of Iran were used in this study

2 Study area The gas field A, is one of the largest gas accumulations in the world containing about 8.5 trillions cubic meters of gas In the Per-sian Gulf zone, the Permian gas basin is known as the Khuff Formation This sequence is composed mainly of carbonate rocks and is extended in Bahrain, Qatar, Abu Dhabi, Saudi Arabia, and Iran (Fig 1)[19] Data in this study came from two wells namely SP5 and SP9 penetrated into the specific reservoir in the field for which FZI data and petrophysical logs were available.Figs 2 and

3show the wells used in this work to build a spatial distribution

of FZI As can be seen, there is a good relationship between FZI with saturation gas and lithology The Dalan formation is divided into

Fig 1 Geographical location of the gas field A.

Fig 2 Well SP9 used in this study to build a 3D distribution of FZI in K4 member of Dalan formation.

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Fig 3 Well SP5 used in this study to build a 3D distribution of FZI in K4 member of Dalan formation.

PLAY

MESOZOIC L.TRIASSIC Seythian

L.Sudair Shale and Clay with

Dolomitic Intercalations

Seal Aghar Mbr Shale

Kangan Mbr Shale K1 Dolomite+Anhydrite

Anhydrite+Dolomite Gas

Limestone

PALEOZOIC PERMIAN

UPPER U Dal

K3 Anhydrite+Dolomite

Anhydrie K4

Dolomite Limestone Anhydrite+Dolomite Gas

Nar Mbr Anhydrite

K5 Limestone+Dolomite

Fig 4 Stratigraphic of Permian-Triassic sequence in the Persian Gulf field.

M Iravani et al / Egyptian Journal of Petroleum xxx (2017) xxx–xxx 3

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four zonations that are composed of biogenic limestone dolomite

and thin bed of anhydrite (Fig 4) In this study, we used the 3D

seismic data to make a 3D HFU model for K4 section of Dalan

formation

3 Methodology

The aim of this study is to apply multi-attribute analysis of

seis-mic data for prediction of FZI throughout the Dalan formation For

this purpose, post stack 3D seismic and well log data of two wells

were used (Figs 1 and 2) Moreover, porosity logs and FZI log were

available for all wells, but check shot data were only available in

one well In order to build a 3D model of FZI the below procedure

was followed based on Rastegarnia and Kadkhodaei-Ilkhichi[16]:

First, an acoustic impedance model was created using model-based inversion that used as an external attribute for the cre-ation of a 3D FZI model;

Then the equation for correlating seismic attributes to FZI log was determined for wells in the field This equation is applied for estimation of the FZI log in the intervals between the wells

Finally, the optimal number of HFUs was determined using the plot of cumulative frequency of FZI for both wells and was gen-eralized on the created 3D HFU model

3.1 Model-based inversion Seismic inversion is a preliminary study in reservoir character-ization Therefore, there is a continuous effort to optimize the inversion algorithm and improve the resolution of the inverted vol-ume The amplitude-based seismic data were processed through a

Fig 5 Characteristic of the extracted wavelet.

Fig 6 Calculated correlation coefficient between real trace seismic and synthetic traces for well sp9.

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Fig 8 Broadband acoustic impedance inversion based on model.

Fig 7 Correlation coefficient resulted in initial model for well sp9.

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model-based inversion algorithm to produce acoustic impedance

volume which was used as an external attribute in the

multi-attribute analysis[20] For this purpose, sonic log data were cor-rected via available seismic data (check shot), geological horizons were determined, picked and interpreted in seismic lines and then best synthetic wavelet and seismogram were extracted Fig 5 shows the extracted identification wavelet Well logs and seismic were calibrated and the highest correlation coefficient between real traces seismic and synthetic traces seismic was obtained using calculated wavelet (Fig 6) Afterwards, an initial model has been developed and inverted for error correction (Fig 7)

To perform the model-based inversion, a geological model was compared with seismic data In order to find a better match, the result of comparison is then used to iteratively update the model Since this model does not use the direct inversion of the seismic data, it is quite applicable[21] In current study, ‘‘hard constraint” method was implemented Indeed, this method considers addi-tional information as a hard constraint that sets absolute bound-aries on how far the final answer may deviate from the initial model In the model-based inversion algorithm, average block size and the number of iterations are of prime importance In this case, using average block size greater than seismic sample interval is a must[21] The logic behind these recommendations is the assump-tion that there might be some false recorded readings from sur-rounded environment that are possibly mixed with the data A larger number of iteration results in better accuracy, even though

it is time consuming process By investigating 3D seismic data from Dalan formation, the following results were obtained; constraint limit was 25%, average block size was 4 ms, and number of itera-tions was set to 10 The results of model-based inversion demon-strate that this type of modeling can clearly identify considered geological layers (Fig 8)

3.2 Constructing a 3D model of FZI

In building a 3D model of FZI, the most important step is the determination of seismic attributes number In this study, the appropriate seismic attributes were selected by stepwise regres-sion and cross validation techniques The stepwise regresregres-sion

Fig 9 The multi-attribute analysis showing the average RMS and validation error;

the optimum number of attributes is equal to 7.

Table 1

Multi-attributes extracted for predicting the FZI.

Target Final attribute Training error

(mm)

Validation error (mm) FZI Acoustic impedance 55.15 74.90

FZI Amplitude envelope 48.70 71.03

FZI Filter 15/20–25/30 45.17 68.171

FZI Filter 45/50–55/60 42.38 84.803

FZI Filter 55/60–65/70 38.6 96.03

FZI Quadrature trace 35.31 69.22

FZI Cosine instantaneous phase 29.94 49.84

Fig 10 The multi-attribute analysis result shows the average RMS error; the optimum operator length is equal to 8.

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Fig 11 Measured FZI logs (in black) and the predicted ones from the multi-attribute analysis (in red); the correlation of the training data is 0.88.

Fig 12 Measured FZI logs (in black) and predicted ones (in red); the correlation of validation data is 0.66.

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method divides dataset into two parts that are training dataset

(original wells, black line in Fig 9) and validating dataset

(pre-dicted data, red line inFig 9) As shown inFig 9, the horizontal

axis represents the number of attributes used in the prediction

and the vertical axis is the corresponding root-mean-squared

pre-diction error This figure demonstrates that the minimum

validat-ing error occurs when only 7 attributes are used The used seismic

attributes and their corresponding prediction errors and

correla-tion coefficients are listed inTable 1 So, seismic attributes consist

of acoustic impedance (AI), quadrature trace, amplitude envelope, filter 15/20–25/30, filter 45/50–55/60, filter 55/60–65/70, and cosine of instantaneous phase were used in this study to estimate flow zone indicator Foregoing filters are frequency internal attri-bute that extracted from raw 3D cube seismic Each row is related

to a particular multi-attribute and the successive row accumulates the previous attributes The value of amplitude envelope attribute

Table 2

The results of the multi-attribute analysis.

Fig 13 FZI and AI volume slice below K4 member of Dalan horizon at 1650 ms.

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is dependent on phase and correlated directly with the change in

acoustic impedance In addition, the instantaneous phase indicator

represents the continuation of the layers The instantaneous phase

is an effective attribute in highlighting discontinuities on seismic

and detection of reflectors, faults, pinch-outs, angularities, and

bed interfaces[16] AI attribute indicates reservoir properties such

as porosity, permeability, and FZI There is a reverse relationship

between AI and FZI FZI is defined as the relationship between

the volumetric proportions of pore space to its geometric

distribu-tion On the other hand, acoustic impedance is a function of both

density and velocity Since deeper layers are harder and denser,

velocity increases with depth Moreover, reduction in wave

veloc-ities and density resulted from porosity and fluid saturation is

leading to decrease in AI Such a correlation can be improved by

applying the residual time-shift between the target FZI logs and

the seismic data

The length parameter is used to resolve the difference between

the frequency content of the target log and seismic attribute

because the log data have high frequency and seismic data have

low frequency with some specified length to relate a group of

neighbouring seismic samples around the point target log (FZI)

In fact, this parameter estimates target log (FZI) by using an aver-age weight of a group of seismic samples on each attribute So, the parameter operator length determines the length of the convolu-tion operator In this study, the optimum value of operator length was determined as high as 8.Fig 10shows where the number of seismic attribute is 7, the optimum value of operator length is 8 where the average error is minimum

After determining the optimum number of attributes and length operator, an equation was found between target log (FZI) and seismic attributes in well locations.Fig 11demonstrates, for the 7th attribute and an eight-point convolution operator, the cor-relation exponent and RMS error between the predicted FZI and actual FZI log in well location are 88% and 29.9mm respectively for the training data.Fig 12shows the target log in black versus the ‘‘predicted” log in red for each well for validation data Finally, relationship between multi-attribute features and the target log was used to predict FZI in inter-well spaces by using seismic data Results obtained from multi-attribute analysis are shown in

Fig 14 FZI and AI volume slice below K4 member of Dalan horizon at 1670 ms.

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Fig 15 FZI and AI volume slice, below K4 member of Dalan horizon at 1700 ms.

Fig 16 Cumulative frequency of FZI and optimal number of HFU categories.

Tiêu đề	Application of Seismic Attribute Technique to Estimate the 3D Model of Hydraulic Flow Units: A Case Study of a Gas Field in Iran
Tác giả	Mohamad Iravani, Mahdi Rastegarnia, Dariush Javani, Ali Sanati, Seyed Hasan Hajiabadi
Trường học	Islamic Azad University, Science and Research Branch
Chuyên ngành	Petroleum Engineering
Thể loại	Research article
Năm xuất bản	2017
Thành phố	Tehran

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